1,721,036 research outputs found
A novel approach for monitoring hydrothermal systems by continuous magnetotelluric observations
No full textUnderstanding the behavior and the evolution of hydrothermal systems is of great interest for both scientific and commercial purposes, such as volcanic hazard assessment and geothermal energy exploitation. To this aim, a novel approach based on continuous magnetotelluric (MT) data is proposed for characterizing and monitoring hydrothermal systems. To test the effectiveness of the proposed approach, a sensitivity analysis has been performed by simulating different evolution scenarios of a hydrothermal system and calculating the MT response at different time intervals corresponding to different stages of the system dynamics. The study proved to be essential for understanding the degree of sensitivity of the MT method to changes of the hydrothermal system with reference to its possible temporal evolutions
SOM clustering analysis in the discrete wavelet transform domain for filtering noisy magnetotelluric data
No full textDespite the magnetotelluric method (MT) is one of the most prominent geophysical technique for deep subsoil exploration, it is not yet completely reliable when applied in urban or industrialized areas due to the presence of anthropic electromagnetic noise. The latter, indeed, may severely affect the MT recordings and, as a consequence, the impedance tensor estimates, which allow to retrieve the apparent resistivity values describing the underground electrical behaviour. In this work, a new filtering approach for MT data denoising is proposed. The procedure is based on the clustering of the impedance tensor estimates by using the Self-Organizing Map (SOM) neural network model. The clustering is performed in the time-frequency domain by a discrete wavelet transformation of the MT signals. In addition, a criterion for selecting, in each wavelet scale, the clusters that lead to the most reliable apparent resistivity estimates has been suggested. The application of the proposed filtering approach to synthetic MT signals has shown that the SOM clustering is very sensitive to the presence of noise and that it is possible to get consistent apparent resistivity curves
On the resolution of electromagnetic induction methods in marine exploration of complex volcanic structures
No full textContribution to the definition of the structural model of the Vesuvius by magnetotelluric measurements
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Magnetotelluric soundings in the southern and SE sectors of Etna
No full textIn ETNATECH, Contract EV5V-CT92-0191, Final Repor
Denoising of magnetotelluric signals by polarization analysis in the discrete wavelet domain
No full textMagnetotellurics (MT) is one of the prominent geophysical methods for underground deep exploration and, thus, appropriate for applications to petroleum and geothermal research. However, it is not completely reliable when applied in areas characterized by intense urbanization, as the presence of cultural noise may significantly affect the MT impedance tensor estimates and, consequently, the apparent resistivity values that describe the electrical behaviour of the investigated buried structures. The development of denoising techniques of MT data is thus one of the main objectives to make magnetotellurics reliably even in urban or industrialized environments. In this work we propose an algorithm for filtering of MT data affected by temporally localized noise. It exploits the discrete wavelet transform (DWT) that, thanks to the possibility to operates in both time and frequency domain, allows to detect transient components of the MT signal, likely due to disturbances of anthropic nature. The implemented filter relies on the estimate of the ellipticity of the polarized MT wave. The application of the filter to synthetic and field MT data has proven its ability in detecting and removing cultural noise, thus providing apparent resistivity curves more smoothed than those obtained by using raw signals
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